{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config ZMQInteractiveShell.ast_node_interactivity='all'\n",
    "from pygoose import *\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "project = kg.Project.discover()\n",
    "feature_list_id = 'wordmatchshare'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_train = kg.io.load(project.preprocessed_data_dir + 'tokens_lowercase_spellcheck_no_stopwords_train.pickle')\n",
    "tokens_test = kg.io.load(project.preprocessed_data_dir + 'tokens_lowercase_spellcheck_no_stopwords_test.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = tokens_train+tokens_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents=[]\n",
    "for pair in tokens:\n",
    "    q1=[' '.join(pair[0])]\n",
    "    q2=[' '.join(pair[1])]\n",
    "    q=[q1,q2]\n",
    "    documents.append(q)\n",
    "\n",
    "# all_questions_flat = np.array(tokens).ravel()\n",
    "# documents = list(set(' '.join(question) for question in all_questions_flat))\n",
    "# del all_questions_flat\n",
    "# documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tfidf_wordMatchShare(pair):#计算所有词的idf\n",
    "    idf={}\n",
    "    q_set = set()\n",
    "    for question in pair:\n",
    "        q1 = str(question[0][0])\n",
    "        if q1 not in q_set:\n",
    "            q_set.add(q1)\n",
    "            words = q1.strip('.').split()\n",
    "            for word in words:\n",
    "                idf[word] = idf.get(word, 0) +1\n",
    "        q2 = str(question[1][0])\n",
    "        if q2 not in q_set:\n",
    "            q_set.add(q2)\n",
    "            words = q2.strip('.').split()\n",
    "            for word in words:\n",
    "                idf[word] = idf.get(word, 0)+1\n",
    "    num_docs = len(pair)\n",
    "    for word in idf:\n",
    "        idf[word] = math.log(num_docs / (idf[word]+1.)) / math.log(2.)\n",
    "    return idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "idf = get_tfidf_wordMatchShare(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_wordMatchShare(question):\n",
    "    q1words={}#q1中每个单词出现的次数\n",
    "    q2words={}\n",
    "    q1 = question[0]\n",
    "    for word in q1:\n",
    "        q1words[word] = q1words.get(word,0)+1\n",
    "    q2 = question[1]\n",
    "    for word in q2:\n",
    "        q2words[word] = q2words.get(word,0)+1\n",
    "    n_shared_word_in_q1 = sum([q1words[w] for w in q1words if w in q2words]) #q1和q2中共同出现的单词的次数\n",
    "    n_shared_word_in_q2 = sum([q2words[w] for w in q2words if w in q1words])\n",
    "    n_tol = sum(q1words.values())+sum(q2words.values())#q1和q2中所有单词出现的次数\n",
    "    sum_shared_word_in_q1 = sum([q1words[w]*idf.get(w, 0) for w in q1words if w in q2words]) #share word的tfidf\n",
    "    sum_shared_word_in_q2 = sum([q2words[w]*idf.get(w, 0) for w in q2words if w in q1words])\n",
    "    sum_tol = sum(q1words[w]*idf.get(w,0) for w in q1words) +sum(q2words[w]*idf.get(w,0) for w in q2words)\n",
    "    \n",
    "    if 1e-6>n_tol:\n",
    "        n_shared_word = 0.\n",
    "    else:\n",
    "        n_shared_word = 1.0*(n_shared_word_in_q1 + n_shared_word_in_q2)/n_tol\n",
    "    if 1e-6>sum_tol:\n",
    "        sum_shared_word = 0.\n",
    "    else:\n",
    "        sum_shared_word = 1.0*(sum_shared_word_in_q1+sum_shared_word_in_q2) /sum_tol\n",
    "    return [n_shared_word, sum_shared_word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  10%|▉         | 275/2751 [02:31<23:25,  1.76it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  19%|█▉        | 535/2751 [05:02<20:47,  1.78it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  29%|██▉       | 795/2751 [07:30<18:56,  1.72it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  38%|███▊      | 1054/2751 [09:54<16:11,  1.75it/s]\u001b[A\n",
      "Batches:  38%|███▊      | 1055/2751 [09:54<16:16,  1.74it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  48%|████▊     | 1310/2751 [12:21<13:43,  1.75it/s]\u001b[A\n",
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      "Batches:  56%|█████▌    | 1528/2751 [14:30<11:25,  1.78it/s]\u001b[A\n",
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      "Batches:  56%|█████▌    | 1531/2751 [14:32<11:34,  1.76it/s]\u001b[A\n",
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      "Batches:  57%|█████▋    | 1556/2751 [14:46<11:12,  1.78it/s]\u001b[A\n",
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      "Batches:  57%|█████▋    | 1565/2751 [14:51<11:00,  1.80it/s]\u001b[A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  57%|█████▋    | 1566/2751 [14:51<10:57,  1.80it/s]\u001b[A\n",
      "Batches:  57%|█████▋    | 1567/2751 [14:52<11:03,  1.78it/s]\u001b[A\n",
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      "Batches:  66%|██████▌   | 1821/2751 [17:14<08:38,  1.79it/s]\u001b[A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  66%|██████▌   | 1822/2751 [17:15<08:37,  1.80it/s]\u001b[A\n",
      "Batches:  66%|██████▋   | 1823/2751 [17:15<08:40,  1.78it/s]\u001b[A\n",
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      "Batches:  67%|██████▋   | 1835/2751 [17:22<08:41,  1.76it/s]\u001b[A\n",
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      "Batches:  67%|██████▋   | 1842/2751 [17:26<08:33,  1.77it/s]\u001b[A\n",
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      "Batches:  67%|██████▋   | 1844/2751 [17:27<08:34,  1.76it/s]\u001b[A\n",
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      "Batches:  67%|██████▋   | 1849/2751 [17:30<08:24,  1.79it/s]\u001b[A\n",
      "Batches:  67%|██████▋   | 1850/2751 [17:30<08:29,  1.77it/s]\u001b[A\n",
      "Batches:  67%|██████▋   | 1851/2751 [17:31<08:27,  1.77it/s]\u001b[A\n",
      "Batches:  67%|██████▋   | 1852/2751 [17:32<08:33,  1.75it/s]\u001b[A\n",
      "Batches:  67%|██████▋   | 1853/2751 [17:32<08:32,  1.75it/s]\u001b[A\n",
      "Batches:  67%|██████▋   | 1854/2751 [17:33<08:33,  1.75it/s]\u001b[A\n",
      "Batches:  67%|██████▋   | 1855/2751 [17:33<08:41,  1.72it/s]\u001b[A\n",
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      "Batches:  68%|██████▊   | 1860/2751 [17:36<08:34,  1.73it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1861/2751 [17:37<08:29,  1.75it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1862/2751 [17:37<08:27,  1.75it/s]\u001b[A\n",
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      "Batches:  68%|██████▊   | 1864/2751 [17:38<08:23,  1.76it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1865/2751 [17:39<08:17,  1.78it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1866/2751 [17:40<08:15,  1.79it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1867/2751 [17:40<08:15,  1.78it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1868/2751 [17:41<08:14,  1.78it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1869/2751 [17:41<08:23,  1.75it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1870/2751 [17:42<08:21,  1.76it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1871/2751 [17:43<08:38,  1.70it/s]\u001b[A\n",
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      "Batches:  68%|██████▊   | 1873/2751 [17:44<08:33,  1.71it/s]\u001b[A\n",
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      "Batches:  68%|██████▊   | 1875/2751 [17:45<08:31,  1.71it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1876/2751 [17:45<08:25,  1.73it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1877/2751 [17:46<08:20,  1.75it/s]\u001b[A\n",
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      "Batches:  68%|██████▊   | 1880/2751 [17:48<08:10,  1.77it/s]\u001b[A\n",
      "Batches:  68%|██████▊   | 1881/2751 [17:48<08:07,  1.79it/s]\u001b[A\n",
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      "Batches:  69%|██████▊   | 1885/2751 [17:50<08:03,  1.79it/s]\u001b[A\n",
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      "Batches:  69%|██████▉   | 1893/2751 [17:55<08:13,  1.74it/s]\u001b[A\n",
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      "Batches:  73%|███████▎  | 1996/2751 [18:53<07:01,  1.79it/s]\u001b[A\n",
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      "Batches:  73%|███████▎  | 2004/2751 [18:57<06:56,  1.79it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2005/2751 [18:58<06:57,  1.79it/s]\u001b[A\n",
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      "Batches:  73%|███████▎  | 2007/2751 [18:59<06:53,  1.80it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2008/2751 [18:59<06:48,  1.82it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2009/2751 [19:00<06:51,  1.80it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2010/2751 [19:00<06:50,  1.80it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2011/2751 [19:01<06:51,  1.80it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2012/2751 [19:02<06:50,  1.80it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2013/2751 [19:02<06:52,  1.79it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2014/2751 [19:03<06:50,  1.79it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2015/2751 [19:03<06:47,  1.81it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2016/2751 [19:04<06:47,  1.81it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2017/2751 [19:04<06:50,  1.79it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2018/2751 [19:05<06:49,  1.79it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2019/2751 [19:05<06:49,  1.79it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2020/2751 [19:06<06:53,  1.77it/s]\u001b[A\n",
      "Batches:  73%|███████▎  | 2021/2751 [19:07<06:56,  1.75it/s]\u001b[A\n",
      "Batches:  74%|███████▎  | 2022/2751 [19:07<06:55,  1.75it/s]\u001b[A\n",
      "Batches:  74%|███████▎  | 2023/2751 [19:08<06:59,  1.74it/s]\u001b[A\n",
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      "Batches:  75%|███████▍  | 2062/2751 [19:30<06:26,  1.78it/s]\u001b[A\n",
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      "Batches:  75%|███████▌  | 2077/2751 [19:38<06:17,  1.79it/s]\u001b[A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  76%|███████▌  | 2078/2751 [19:39<06:18,  1.78it/s]\u001b[A\n",
      "Batches:  76%|███████▌  | 2079/2751 [19:39<06:16,  1.79it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  85%|████████▍ | 2334/2751 [22:04<03:51,  1.80it/s]\u001b[A\n",
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      "Batches:  94%|█████████▍| 2588/2751 [24:26<01:30,  1.79it/s]\u001b[A\n",
      "Batches:  94%|█████████▍| 2589/2751 [24:26<01:30,  1.79it/s]\u001b[A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batches:  94%|█████████▍| 2590/2751 [24:27<01:29,  1.80it/s]\u001b[A\n",
      "Batches:  94%|█████████▍| 2591/2751 [24:27<01:28,  1.80it/s]\u001b[A\n",
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      "Batches:  94%|█████████▍| 2599/2751 [24:32<01:25,  1.78it/s]\u001b[A\n",
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      "Batches:  95%|█████████▌| 2619/2751 [24:43<01:13,  1.81it/s]\u001b[A\n",
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      "\u001b[A"
     ]
    }
   ],
   "source": [
    "features = kg.jobs.map_batch_parallel(\n",
    "    tokens,\n",
    "    item_mapper=get_wordMatchShare,\n",
    "    batch_size=1000,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train = np.array(features[:len(tokens_train)], dtype='float64')\n",
    "features_test = np.array(features[len(tokens_train):], dtype='float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\n",
    "    'wordmatchshare_percent',\n",
    "    'wordmatchshare_tfidf'\n",
    "]\n",
    "project.save_features(features_train, features_test, feature_names, feature_list_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
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